Welcome to RE-SAT
Renewable Energy Analytics Platform
To increase the penetration of renewable energy, governments, investors, developers, IPPs and energy companies need confidence that the complex weather variables driving renewable power around the world are well understood and modelled accurately.
RE-SAT meets this need by providing high resolution global weather data and comprehensive analysis in an intuitive, attractive cloud-based energy analytics platform. Pricing is competitive, based on spatial resolution and geographic coverage.
RE-SAT can simulate simple or complex renewable generation scenarios, turning the best available global weather data into bankable planning metrics.
Whether you are assessing single sites or developing complex 30 year national energy roadmaps, RE-SAT provides rigorous scientific data to help ensure your project maximises the operational return on investment.
RE-SAT is being developed by the Institute for Environmental Analytics. As a leading edge centre for environmental data science, we are using our expertise in understanding the complexities of a changing environment to drive the uptake of renewable energy.
The result is high quality, bespoke weather data which enables more reliable energy metrics at the pre-feasibility stage: increasing transparency, reducing uncertainty, mitigating risk, and improving project bankability.
The left animation shows 3 days of RE-SAT high resolution wind simulations for Palau. The right animation shows the corresponding wind data from the global ERA5 dataset.
RE-SAT weather data at 1km spatial resolution and 10-minute timestep provides a far richer simulation of local weather, capturing both topographically-related spatial variability and variability relating to the land/sea divide. This detail is important for accurately analysing the available variable renewable resource, and subsequently the generation potential.
The large grid squares in the global weather data are not appropriate for simulations at the local scale.
Through graphs and charts, users can explore summary statistics in detail using the generation explorer, demand explorer and generation heatmap.
Scenarios can be copied and shared enabling iterative, collaborative working across project teams.
Users can switch exceedance probabilities (p10, p25, p50, p75 and p90) and have metrics updated in real time.
A highly successful proof-of-concept led to a subsequent 4-year build phase to operationalise and scale the proof-of-concept platform. This work is also being supported by the UK Space Agency IPP and is due for completion in 2021. The build phase is being completed collaboratively with the governments and national energy utility companies in Saint Lucia, Montserrat, Tonga, Palau, Vanuatu, and Mauritius.
There are already 170 registered users of the basic RE-SAT platform.
A substantially upgraded platform version will launch in early 2021 as a fully developed commercial offering.
The IEA is a leading edge centre for environmental data analytics. Formed in 2015 with a £5.6m investment, we have now grown to 35 staff with c.£10m of additional revenue won. Our office is located on the University of Reading campus in the United Kingdom - one of Europe’s largest centres for weather and climate research - and next to the European Centre for Medium Range Weather Forecasting (ECMWF).
Our entrepreneurial team is comprised of PhD-educated data scientists, software developers and UX design/visualization experts with backgrounds in the fields of physics, maths, statistics, climate science and engineering. Together we share a common passion for the environment and a deep understanding of how to turn scientific data into meaningful information for end users through data analytics and the building of visually appealing, responsive and easy-to-use software applications.
Our renewable energy projects are focused on understanding how the weather and climate affect production. This requires experience in handling large datasets, cloud computing and advanced weather modelling. Our data scientists are adept at using numerical weather prediction models and combining these with in-situ observations, satellite data and machine learning techniques to produce easy-to-use but scientifically rigorous products to help clients understand and simulate their renewable energy future.